About Tianhai Zu

Download my CV here

Hi! I am Tianhai Zu, an assistant professor in the Department of Management Science and Statistics at the University of Texas at San Antonio. I earned my Ph.D. degree in Business Analytics from the University of Cincinnati, Lindner College of Business. I am particularly interested in developing novel methodologies and useful computational tools for complex data; and answering difficult and consequential questions in business and healthcare.

Teaching Interests

Business Analytics, Machine Learning, Data Analytic Methods, Data Mining, Data Wrangling, Network Analysis, Text Mining, Big Data Technologies.

Course Websites

BANA7025 Data Wrangling

BANA7038 Data Analytics Methods

Research Interests

Ultra-high Dimensional Variable Selection, Machine Learning, Dimension Reduction, Uncertainty in Financial Bankruptcy, Network Inference and Social Network Analysis, Big Data Technologies, Information Systems.

Journal Publications

Zu, T., Qin, Y. (2024), “Local Bootstrap for Networks,” Biometrika.

Zu, T., Green, B., Lian, H., Yu, Y. (2023), “Ultra-high Dimensional Quantile Regression for Longitudinal Data: an Application to Blood Pressure Analysis,” Journal of the American Statistical Association, forthcoming. [Paper] [Slides]

Green, B., Lian, H., Yu, Y., Zu, T. (2023), “Semiparametric Penalized Quadratic Inference Functions for Correlated Data in Ultra-high Dimensions,” Journal of Multivariate Analysis, forthcoming. [Paper]

Green, B., Lian, H., Yu, Y., Zu, T. (2020) “Ultra-high Dimensional Semiparametric Longitudinal Data Analysis,” Biometrics, 77(3), 903-913. [Paper]

Zu, T. and Yu, Y. (2021), “SIQR: An R Package for Single-index Quantile Regression,” R Journal. [Paper] [R Package]

Srinivasan, SM., Sangwan, R., Neill, C., Zu, T. (2019), “Power of Predictive Analytics: Using Emotion Classification of Twitter Data for Predicting 2016 US Presidential Elections,” The Journal of Social Media in Society, 8,1,211-230. [Paper]

Papers under Revision

Zu, T., Yu, Y., “GPLSIM: An R Package for Penalized Spline Estimation for Generalized Partially Linear Single-index Models,” revised and resubmitted to R Journal. [Paper] [R package]

Research in Progress

“Multivariate High Dimensional Binary/Matrix Response Regression,” with Yan Yu and Heng Lian, targeting Journal of the American Statistical Association.

“Estimate Networks via Local Structure,” with Yichen Qin, targeting Electronic Journal of Statistics.

“Determinants of Corporate Bankruptcy: Identification and Uncertainty,” with Yichen Qin and Yan Yu, targeting Management Science.

“Municipal Securities and Bailouts,” with Zhenfeng Peng and Qiongwen Lei, targeting Journal of Banking and Finance.

“Analyzing Conflicting Information via Multi-dimensional Textual Network Analysis Framework,” with Zewei Lin, targeting Management Science.

Peer-reviewed Conference Publications

Harrison, A., Samuel, B., Shan Z., Cook M., Zu T., Dawani D. (2019), “Learning to See the Hook: Comparing Phishing Training Approaches,” ICIS 2019 Proceedings.

Book Chapters

Qiu R.G., Zu T., Qian Y., Qiu L., Badr Y. (2019), “Leveraging Big Data Platform Technologies and Analytics to Enhance Smart City Mobility Services.” In: Maglio P., Kieliszewski C., Spohrer J., Lyons K., Patrício L., Sawatani Y. (eds) Handbook of Service Science, Volume II. Service Science: Research and Innovations in the Service Economy. Springer, Cham.

R Packages

An R Package for Single-index Quantile Regression [Package] [Paper]

GPLSIM: An R Package for Penalized Spline Estimation for Generalized Partially Linear Single-index Models [Package] [Paper]

Tutorials

Parallel Computing with R